Research Journal of Applied Sciences, Engineering and Technology 7(24): 5157-5162, 2014
ISSN: 2040-7459; e-ISSN: 2040-7467
© Maxwell Scientific Organization, 2014
Submitted: February 08, 2014
Accepted: April ‎09, ‎2014
Published: June 25, 2014
Effect of Different Places in Applying Laplacian Filter on the Recovery Algorithm in
Spatial Domain Watermarking
1
Saeed Amirgholipour, 2Vahid Saffari, 1Aboosaleh Mohammad Sharifi and 1Ali Hasiri
Department of Computer, Ramsar Branch, Islamic Azad University, Ramsar, I.R. Iran
2
Department of Computer, Collage of Engineering, Shiraz Branch, Islamic Azad University,
Shiraz, I.R. Iran
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Abstract: Generally, Laplacian filter is used to make an image more defined and enhanced. In this study, a
comparison is toke place between the effects of different places of performing Laplacian filter on the power of the
watermark recovery in spatial domain image watermarking. This filter is applied in two different places in the
watermark recovery algorithm; before performing the watermark recovery and before take the correlation in the
middle of recovery algorithm. The distinction between the watermark and UN water marked parts of the image are
increased by this filter. Thus, watermark could recover significantly better by recovery algorithm. We intend to
determine which of these places is more appropriate to apply this filter. A typical correlation based method is used
as a representative of spatial domain watermarking methods. Several experiment are done to compare the effect of
different places in applying proposed filter on quality of extracted watermark in correlation based watermarking
algorithm.
Keywords: Correlation based watermarking, digital image watermarking, Laplacian filter, spatial domain
watermarking
INTRODUCTION
In recent years, with the expansion of Internet, it
becomes very easy to transmit and distribute digital
media. Therefore, there is the high demand for financial
and intellectual property protection methods for digital
data. The watermarking has been proposed as an
appropriate proposal for this problem. The purpose of
the watermark is to embed some extra information
about the digital data without visibly modifying it
(Kasmani and Naghsh-Nilchi, 2008).
In the design of the watermarking algorithm
always, there is a conflict between robustness and
imperceptibility. The imperceptibility means that how
much the embedded information makes reductions in
signal quality; and the robustness is the ability of the
watermark to remain readable after innocent or
malicious signal processing operations on the
watermarked image. These parameters are incompatible
with each other and they should be set to meet the
requirements of the application (Kasmani et al., 2009;
Kasmani and Naghsh-Nilchi, 2008).
Generally, watermarking methods could be
categorized into the spatial domain or the transform
domain. The watermark information is embedded
directly in the pixels of the host image in the spatial
domain techniques. These methods are not robust to
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image common image processing operation (Kasmani
and Sharifi, 2014; Potdar et al., 2005). Although some
methods, e.g., (Depovere et al., 1998) utilized the filters
capability to better extract the watermark. Transform
domain watermarking schemes benefit properties of the
transform domain to embed the watermark. These
methods usually use the Discrete Cosine Transform
(DCT) (Chu, 2003; Lin and Chen, 2000) and the
Discrete Wavelet Transform (DWT) (Hsieh et al.,
2001). These methods typically bring higher image
fidelity and more robustness to image manipulations.
Many researchers have tried to improve the
performance of algorithms watermarking. Several of
these researchers investigate on finding suitable
locations for watermark embedding and others have
studied the enhancement algorithm to increase power of
watermark retrieval algorithms.
In the first methods, there are an attempt to use
human visual system characteristics to choose
appropriate for resistance and transparency. These
methods are commonly used in frequency domain
watermarking techniques. These method utilize a
perceptually optimal quantization matrix (Watson,
1994), Just Noticeable Difference (JND) (Chou and Li,
1995), wavelet filter (Watson et al., 1997) and Human
Visual System (HVS) (Kutter and Winkler, 2002;
Levický and Peter, 2004), for probing the most suitable
coefficients to embed the watermark information.
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Corresponding Author: Saeed Amirgholipour, Department of Computer, Ramsar Branch, Islamic Azad University, Ramsar,
I.R. Iran
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However, in the second methods, there is an
attempt at providing a method to make watermark
information visible to the watermark recovery
algorithms. As a result, these approaches could increase
the resistance watermarking algorithms.
An improved detector is proposed for detection
based on thresholds extracted by statistic rules on which
the method relies (Fotopoulos and Skodras, 2002).
Applying blurring filters to a watermarked image
before executing watermark detection can increase the
possibility of detection (Braudaway and Mintzer, 2003).
Since blurring filters, suppress the high spatial
frequencies, they generally distort the image quality.
However, for a watermark that has dominant low
frequency content, the application of a blurring filter
can serve to improve the statistical environment for
watermark detection and thereby improves the detection
probability. Since the content of image might interfere
with the watermark, especially in the low-frequency
parts, the reliability of the detector could be improved
by applying matched filtering before correlation
(Depovere et al., 1998). This decreases the influence of
the original image to the correlation. Therefore, the
watermark could easily be extracted from watermarked
image.
As a category of Blind Embedding Watermark,
Hafiz proposed an approach to blind watermark
detection/decoding for spread spectrum by using of
Independent Component Analysis theory (Malik et al.,
2005). It uses the theory of Independent Component
Analysis (ICA) and detects the watermark with a blind
source separation method. The watermark information
is considered as noise for the watermarked image in its
spatial domain. This noise is magnified before detection
and then recovers the watermark information by
adjusting the extracted data from the frequency domain
according to the global minimum method (Pan et al.,
2004). A preprocessing method is proposed that exploit
a combination of noise boosting and filtering to
facilitate recovering the watermark from watermarked
image in the DCT-based watermarking algorithm
(Kasmani et al., 2009; Kasmani and Sharifi, 2014).
In this study, a comparison is made between effects
of different places of applying Laplacian filter on
increasing power of correlation based watermark
recovery algorithms in the spatial domain methods. In
order to compare, this filter is applied before executing
watermark extraction procedures and before comparing
the correlation between the extracted block and pseudo
random noise, in the correlation based method.
Different Experiments are done to show that which of
these places is appropriate for applying Laplacian filter
in the spatial domain based watermarking.
MATERIALS AND METHODS
Laplacian filter: In this study, Gonzales definition is
used for Laplacian filter (Gonzalez and Woods, 2002).
Because the Laplacian is a derivative operator, its use
highlights gray-level discontinuities in an image and
deemphasizes regions with slowly changing gray levels.
This will tend to produce images that have grayish edge
lines and other discontinuities, all superimposed on a
dark, featureless background. Background features can
be “recovered” while still preserving the sharpening
effect of the Laplacian operation simply by adding the
original and Laplacian images.
In this study a special implementations of the
Laplacian is used:
 f ( x − 1, y − 1) + f ( x − 1, y ) 


 + f ( x − 1, y + 1) + f ( x, y − 1) 
2
f 8 f ( x, y ) − 
∇
=

 + f ( x, y + 1) + f ( x + 1, y − 1) 
 + f x + 1, y + f x + 1, y + 1 
) (
)
 (
(1)
This equation can be implemented using the mask
shown in Eq. (2):
 −1 −1 −1
 −1 8 −1


 −1 −1 −1
(2)
Correlation based watermarking using block
processing in the spatial domain:
Watermark embedding algorithm: The watermark
embedding process is represented in Fig. 1, followed by
a detailed explanation:
Step 1: Divide the host image into 16×16 blocks.
Step 2: Re-formulate the watermark image into a
vector of zeroes and ones.
Step 3: Generate two uncorrelated pseudorandom
sequences by a key. One sequence is used to
embed the watermark bit 0 (PN_0) and the
other sequence is used to embed the
watermark bit 1 (PN_1). Number of elements
in each of the two pseudorandom sequences
must be equal to the number of block.
Step 4: Embed the two pseudorandom sequences,
PN_0 and PN_1, with a gain factor α in the
16×16 blocks of the host image. If we donate
X as the matrix of the block, then embedding
is done as Eq. (3):
 X + α * PN 0 watermark _ bit = 0
X '= 
 X + α * PN1 watermark _ bit = 1
(3)
Watermark extracting procedure: The typical
correlation based algorithm is a blind watermarking
algorithm and thus the original host image is not
required to extract the watermark. Extraction algorithm
is the same as embedding one and filtering is used
before applying it to better separate watermark
information from host image. The watermark extraction
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Fig. 1: The watermark embedding process
(a)
(b)
Fig. 2: (a) the watermark extracting with applying Laplacian before preforming extracting algorithm, (b) the watermark
extracting process with applying Laplacian in the middle of extracting algorithm
procedure is shown in Fig. 2 and described in details in
the following steps:
Step 5: The scrambled watermark is reconstructed
using the extracted watermark bits.
Step 1: Applying proposed filter as shown in the
Eq. of (2) for Laplacian filter, on the
watermarked image.
Step 2: Divide watermarked image that could be
attacked or not into 16×16 blocks.
Step 3: Regenerate the two pseudorandom sequences
(PN_0 and PN_1) using the same key which
used in the watermark embedding procedure.
Step 4: For each block in the watermarked image
calculate the correlation between the element
and the two generated pseudorandom
sequences (PN_0 and PN_1). If the correlation
with the PN_0 was higher than the correlation
with PN_1, then the extracted watermark bit is
considered 0, otherwise the extracted
watermark is considered 1.
RESULTS AND DISCUSSION
To compare the efficiency of the proposed filter on
correlation based methods, three standard gray-scale
images with different contents of size 512×512 are used
in our experiments, as shown in Fig. 3a to c. Pepper is
used as a representation of image with low spatial
frequency and Barbara as a representation of image
with average spatial frequency and Baboon as a
representation of image with high spatial frequency. In
this experiment, a 32×32 binary image, as shown in
Fig. 3d is taken as the watermark of images. The effect
of the Laplacian filters is investigated by measuring
imperceptible and robustness of watermarked image.
For the imperceptible capability, a quantitative index,
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Res. J. Appl. Sci. Eng. Technol., 7(24): 5157-5162, 2014
filtering. The presented method is implemented using
MATLAB.
(a)
(b)
(c)
(d)
Fig. 3: (a) the original pepper image, (b) the original Barbara
image, (c) the original Baboon image and (d) the
original watermark
Peak Signal-to-Noise Ratio (PSNR), is employed to
evaluate the difference between an original image O
and a watermarked image. For the robust capability, the
Mean Absolute Error (MAE) measures the difference
between an original watermark W and the
corresponding extracted one. If a method has lower
MAE, it is more robust. The PSNR and the MAE are,
respectively, defined by Eq. (4) and (5); respectively:
255 × 255
PSNR ( O,O ) = 10 log
10 I − 1 J − 1
2
O −O
∑ ∑
ij
ij
=
i 0=
j 0
(
)
I×J
(4)
S −1
MAE (W , Wˆ ) =
∑
wi − wˆ i
1
i =0
W
(5)
where, ||.|| 1 and |.| stand for the L1 norm and the number
of components of a vector, respectively.
The watermarked image O is obtained following
the completion of the watermark embedding procedure.
The watermark information is embedded with PSNR
30, 35 and 40 dB, respectively in the watermarked
images. Then Laplacian filter which is described in
above Section are performed on these watermarked
images which may be attached by the method presented
in this section. MAE between the original W and the
extracted watermark W' is calculated for different
PSNRs. The performance of the Laplacian filter is
compared with the default results when no processing is
done on the correlation based watermarking algorithm.
To compare the robustness of this filter, the algorithm is
tested by several attacks, including JPEG compression,
image scaling, adding Salt and Pepper noise, Gaussian
R
R
Visual comparison: The Table 1 shows visual
comparisons between the effects of different places in
applying Laplacian filter on the extracted watermark in
the correlation based watermarked. These results have
been obtained for Pepper's image which watermarked
with PSNR 40. As shown in the Table 1, although
applying the Laplacian filter before correlation improve
the reliability of watermark recovery of typical
correlation based watermarking method, But,
preforming the Laplacian filter before running
extraction slightly is better than it. This improvement in
the case of noise addition attack is more significant than
the others. From these experimental results, we could
find that the applying Laplacian filter before running
extraction is more appropriate.
Results for the pepper image: In the second
experiment, the results of applying Laplacian filter on
Pepper image as a representation of image with low
spatial frequency are shown. The goal of this section is
to show the effect of proposed filter on a typical low
spatial frequency image. As it is shown in the Table 2,
results are significantly increased by applying the
Laplacian filter in both of places for in compare with
normal extraction. Applying Laplacian filter before
recovery algorithms is more successful to the attacked
image with jpeg compression and a Blurring attacks.
However, in the noise addition and resize attacks,
utilizing Laplacian filter before calculating the
correlation better improves the results. As it shown in
this table, preforming Laplacian filter before recovery
algorithm shows better improvement in the values of
MAE. For example, in jpeg compression attack on
image with PSNR 40 as a high imperceptible
watermarked image, MAE preforms 0.035 much better
than utilized this filter before calculating the correlation
and in the case of Blurring attack improvement is 0.05
in term of MAE. Therefore, for low spatial frequency,
utilizing the Laplacian before running extraction is
more suitable than preforming it before calculating the
correlation.
Results for the Barbara image: In the third
experiment, the results of applying Laplacian filters on
Table 1: Visual comparison between the extracted watermark from the peppers watermarked image with PSNR = 40
Attack
-------------------------------------------------------------------------------------------------------------------------------------------Jpeg compression
Salt and pepper
Blurring (Gaussian
Attack free
(50%)
Scaling (50%)
noise (10%)
with h = 5 and σ = 1)
Without
filtering
Laplacian before
extraction
Laplacian before
correlation
g
g
g
g
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Res. J. Appl. Sci. Eng. Technol., 7(24): 5157-5162, 2014
Table 2: Comparison in term of MAE between the extracted watermark from the peppers watermarked image with diffrent methods
Attack
------------------------------------------------------------------------------------------------------------------------Jpeg compression
Salt and pepper
Blurring (Gaussian
Pepper image
PSNR
Attack free
(50%)
Scaling (50%)
noise (10%)
with h = 5 and σ = 1)
Without filtering
30
0.0107
0.1055
0.2285
0.1973
0.2910
35
0.0381
0.2051
0.2959
0.3154
0.3389
40
0.1045
0.3428
0.3662
0.4316
0.4092
Laplacian before
30
0
0.0029
0.0498
0.0273
0.0459
extraction
35
0
0.0449
0.0947
0.0928
0.0898
40
0.0020
0.1838
0.2207
0.2207
0.1768
Laplacian before
30
0
0.0049
0.0381
0.0225
0.0654
correlation
35
0
0.0537
0.0850
0.0879
0.1152
40
0.0020
0.2178
0.2148
0.2148
0.2246
Table 3: Comparison in term of MAE between the extracted watermark from the Barbara watermarked image with diffrent methods
Attack
------------------------------------------------------------------------------------------------------------------------Jpeg compression
Salt and pepper
Blurring (Gaussian
Attack free
(50%)
Scaling (50%)
noise (10%)
with h = 5 and σ = 1)
Barbara image
PSNR
Without filtering
30
0.0186
0.1338
0.2793
0.1914
0.3213
35
0.0879
0.2734
0.3428
0.3311
0.3721
40
0.1982
0.3984
0.3867
0.4258
0.4209
Laplacian before
30
0.0049
0.0332
0.0879
0.0635
0.0957
extraction
35
0.0254
0.1240
0.1816
0.1514
0.1895
40
0.0771
0.2646
0.2852
0.2695
0.2881
Laplacian before
30
0.0049
0.0420
0.0762
0.0566
0.1270
correlation
35
0.0273
0.1328
0.1719
0.1445
0.2285
40
0.0713
0.2695
0.2803
0.2607
0.3105
Table 4: Comparison in term of MAE between the extracted watermark from the Baboon watermarked image with diffrent methods
Attack
------------------------------------------------------------------------------------------------------------------------Jpeg compression
Salt and pepper
Blurring (Gaussian
Baboon image
PSNR
Attack free
(50%)
Scaling (50%)
noise (10%)
with h = 5 and σ = 1)
Without filtering
30
0.0000
0.0449
0.2432
0.1475
0.3027
35
0.0381
0.1602
0.3379
0.3018
0.3730
40
0.0977
0.2500
0.3809
0.3691
0.4043
Laplacian before
30
0.0000
0.0186
0.1201
0.0352
0.1309
extraction
35
0.0156
0.0918
0.2188
0.1563
0.2314
40
0.0430
0.1963
0.2949
0.2412
0.2949
Laplacian before
30
0.0000
0.0234
0.1152
0.0381
0.1494
correlation
35
0.0127
0.1094
0.2227
0.1416
0.2559
40
0.0439
0.2158
0.2959
0.2354
0.3193
Barbara as a representation of image with average
spatial frequency. The goal of this section is to show
the effect of proposed filter on a typical average spatial
frequency image. As it is shown in the Table 3, results
are significantly improved by convolution of the
Laplacian filter with the Barbara image in both places
in compare with normal extraction. Convolving
Laplacian filter before recovery algorithms is more
successful to the attacked image with jpeg compression
and a Blurring attacks. However, in the noise addition
and resize attacks, apply filter before calculating the
correlation better improves the results. As it shown in
this table, applying Laplacian filter before recovery
algorithm shows better improvement in the values of
MAE. For example, in blurring attack on image with
PSNR 40 as a high imperceptible watermarked image,
MAE performs 0.025 much better than applying this
filter before calculating the correlation. Therefore, for
average spatial frequency, convolving the Laplacian
before running extraction is more appropriate than
performing it before calculating the correlation.
Results for the baboon image: In the fourth
experiment, the results of applying Laplacian filter on
Baboon image as a representation of image with high
spatial frequency are shown. In this section, we
concentrate to show the effect of proposed filter on a
typical high spatial frequency image. As it is shown in
the Table 4, results are significantly improved by
convolution of the Laplacian filter with the Baboon
image in both places in compare with normal recovery
method. Convolving Laplacian filter before recovery
algorithms is more successful to the attacked image
with jpeg compression and blurring attacks and slightly
in the scaling attack. However, in the noise addition
attack, preform filter before calculating the correlation
better improves the results slightly. As it shown in this
table, convolving Laplacian filter before extraction
algorithm shows better improvement in the values of
MAE. For example, in blurring attack on image with
PSNR 40 as a high imperceptible watermarked image,
MAE performs 0.025 much better than applying this
filter before calculating the correlation and in jpeg
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Res. J. Appl. Sci. Eng. Technol., 7(24): 5157-5162, 2014
compression this value is 0.02 better than the other
method. Therefore, for high spatial frequency,
convolving the Laplacian before running recovery is
more appropriate than convolving it before calculating
the correlation.
CONCLUSION
The dissimilarity between watermarked part and un
water marked part of host image is increased by
Laplacian filter. Then, this enhanced watermarked
image is used as the input image in the watermark
extraction's process. In this study, a comparison
between the effects of different places on convolving
Laplacian filter with the watermarked image in
increasing the power of watermark recovery algorithms
is investigated. The Laplacian filter is convolved with
watermarked image in two places; before executing
recovery algorithm and in the middle of recovery
algorithm and before calculates the correlation. Several
experiments are done to show that which of these
places is more suitable for improving power of spatial
domain watermarking method. Effectiveness of the
methods is tested by comparing its result with each
other in the term of MAE. The watermark is extracted
after common image processing attacks with lower
MAE value by performing Laplacian filter before
recovery algorithm. Especially, increasing in
performance is become more noticeable in case of
enhancement operations with blurring filter and jpeg
compression. In high spatial frequency image,
improvement is better than low and average frequency
images. However, results of performing Laplacian filter
before take the correlation is better than applying filter
before executing recovery algorithm, in the case of
resizing and salt and pepper noise. Therefore, we
suggest to use Laplacian filter before running
watermark recovery algorithms in the spatial domain
watermarking algorithm.
REFERENCES
Braudaway, G.W. and F.C. Mintzer, 2003. Application
of blurring filters to improve detection of invisible
image watermarks. Proceedings of SPIE, 5020:
269-277.
Chou, C.H. and Y.C. Li, 1995. A perceptually tuned
subband image coder based on the measure of justnoticeable-distortion profile. IEEE T. Circ. Syst.
Vid., 5(6): 467-476.
Chu, W.C., 2003. DCT-based image watermarking
using subsampling. IEEE T. Multimedia, 5(1):
34-38.
Depovere, G., T. Kalker and J.P. Linnartz, 1998.
Improved watermark detection reliability using
filtering before correlation. Proceedings of
International Conference on Image Processing
(ICIP, 1998), pp: 430-434.
Fotopoulos, V. and A.N. Skodras, 2002. Improved
watermark detection based on similarity diagrams.
Signal Process-Image, 17(4): 337-345.
Gonzalez, R.C. and R.E. Woods, 2002. Digital Image
Processing: Introduction. 2nd Edn., Prentice Hall,
Upper Saddle River, NJ.
Hsieh, M.S., D.C. Tseng and Y.H. Huang, 2001. Hiding
digital watermarks using multiresolution wavelet
transform. IEEE T. Ind. Electron., 48(5): 875-882.
Kasmani, S.A. and A. Naghsh-Nilchi, 2008. A new
robust digital image watermarking based on joint
DWT-DCT transformation. Proceeding of 3rd
International Conference on Convergence and
Hybrid Information Technology (ICCIT '08), 2:
539-544.
Kasmani, S.A., M. Mahfouzi and M. Asfia, 2009. A
new pre-processing approach to improve DCTbased watermarkings extraction. Proceeding of
International Association of Computer Science and
Information
Technology-Spring
Conference
(IACSITSC'09), pp: 131-135.
Kasmani, S.A. and A.M. Sharifi, 2014. A pre-filtering
method to improve watermark detection rate in
DCT based watermarking. Int. Arab J. Inform.
Technol., 11(2): 178-185.
Kutter, M. and S. Winkler, 2002. A vision-based
masking model for spread-spectrum image
watermarking. IEEE T. Image Process., 11(1):
16-25.
Levický, D. and F. Peter, 2004. Human visual system
models in digital image watermarking. Radio Eng.,
13(4): 38-43.
Lin, S.D. and C.F. Chen, 2000. A robust DCT-based
watermarking for copyright protection. IEEE
T. Consum. Electr., 46(3): 415-421.
Malik, H., A. Khokhar and R. Ansari, 2005. Improved
watermark detection for spread-spectrum based
watermarking using independent component
analysis. Proceeding of the 5th ACM Workshop on
Digital Rights Management, pp: 102-111.
Pan, Z., L. Li, M. Zhang and D. Zhang, 2004.
Watermark extraction by magnifying noise and
applying global minimum decoder. Proceeding of
IEEE 1st Symposium on Multi-agent Security
Survivability, pp: 349-352.
Potdar, V.M., H. Song and C. Elizabeth, 2005. A
survey of digital image watermarking techniques.
Proceeding of 3rd IEEE International Conference
on Industrial Informatics (INDIN'05), pp: 709-716.
Watson, A.B., 1994. Perceptual optimization of DCT
color quantization matrices. Proceeding of IEEE
International Conference Image Processing (ICIP,
1994), pp: 100-104.
Watson, A.B., G.Y. Yang, J.A. Solomon and
J. Villasenor, 1997. Visibility of wavelet
quantization noise. IEEE T. Image Process., 6(8):
1164-1175.
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